Spatio-temporal Parking Behaviour Forecasting and Analysis Before and During COVID-19

TL;DR

This study introduces a spatial-aware parking prediction framework that models spatial and temporal dependencies to improve occupancy forecasting, especially during irregular periods like COVID-19, revealing pandemic impacts on parking behavior.

Contribution

The paper presents a novel spatial connection graph construction and spatio-temporal forecasting method for parking prediction, addressing the gap of spatial correlation modeling in previous studies.

Findings

The proposed method outperforms baseline models in parking occupancy forecasting.

COVID-19 significantly affected parking behavior and demand patterns.

Modeling spatial dependence improves prediction accuracy during irregular periods.

Abstract

Parking demand forecasting and behaviour analysis have received increasing attention in recent years because of their critical role in mitigating traffic congestion and understanding travel behaviours. However, previous studies usually only consider temporal dependence but ignore the spatial correlations among parking lots for parking prediction. This is mainly due to the lack of direct physical connections or observable interactions between them. Thus, how to quantify the spatial correlation remains a significant challenge. To bridge the gap, in this study, we propose a spatial-aware parking prediction framework, which includes two steps, i.e. spatial connection graph construction and spatio-temporal forecasting. A case study in Ningbo, China is conducted using parking data of over one million records before and during COVID-19. The results show that the approach is superior on parking occupancy forecasting than baseline methods, especially for the cases with high temporal irregularity such as during COVID-19. Our work has revealed the impact of the pandemic on parking behaviour and also accentuated the importance of modelling spatial dependence in parking behaviour forecasting, which can benefit future studies on epidemiology and human travel behaviours.